In Vitro Conversion Assays Diagnostic for Neurodegenerative Proteinopathies
Bibliographic record
Abstract
BACKGROUND: In vitro conversion assays, including real-time quaking-induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA) techniques, were first developed to study the conversion process of the prion protein to its misfolded, disease-associated conformation. The intrinsic property of prion proteins to propagate their misfolded structure was later exploited to detect subfemtogram quantities of the misfolded protein present in tissues and fluids from humans and animals with transmissible spongiform encephalopathies. Currently, conversion assays are used clinically as sensitive and specific diagnostic tools for antemortem diagnosis of prion disease. CONTENT: In vitro conversion assays are now being applied to the development of diagnostics for related neurodegenerative diseases, including detection of misfolded α-synuclein in Parkinson disease, misfolded amyloid-β in Alzheimer disease, and misfolded tau in Pick disease. Like the predicate prion protein in vitro conversion diagnostics, these assays exploit the ability of endogenously misfolded proteins to induce misfolding and aggregation of their natively folded counterpart in vitro. This property enables biomarker detection of the underlying protein pathology. Herein, we review RT-QuIC and PMCA for (a) prion-, (b) α-synuclein-, (c) amyloid-β-, and (d) tau-opathies. SUMMARY: Although already in routine clinical use for the detection of transmissible spongiform encephalopathies, in vitro conversion assays for other neurodegenerative disorders require further development and evaluation of diagnostic performance before consideration for clinical implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".